The Path of Integration between Crypto and AI: From Computing Power Aggregation to Agent Economy

Decentralized Computing Power: An Initial Exploration of the Combination of Crypto and AI

As the wave of artificial intelligence sweeps across the globe, the Crypto world is also searching for its own access path. Initially, the combination of the two primarily focused on the aggregation of decentralized Computing Power resources. This approach leverages blockchain technology to coordinate globally idle GPU and CPU resources, matching supply and demand, reducing costs, and providing transparent and fair incentive mechanisms for resource contributors.

At that time, centralized cloud service prices were high and resources were exclusive, while small and medium developers and the long tail market had an increasing demand for flexible and low-threshold Computing Power. The decentralized nature of Crypto became a breakthrough. The exploration in this stage exhibited distinct characteristics:

1. Targeting the long-tail market
2. Emphasize Flexibility
3. Explore New Mechanisms
4. Combining Decentralized Physical Infrastructure Networks (DePIN)

Among them, the most innovative case is a project that modularizes AI service through a pioneering subnet structure. Each subnet has its own independent community of miners and validators, with its token serving as the main ecological fuel, allowing users to participate in the ecosystem and earn rewards in various ways.

However, the attempts at this stage also reveal obvious limitations: the pure Computing Power market competition has fallen into a price war, the decentralization performance of the reasoning layer is insufficient, and the supply and demand matching lacks application layer narrative. Crypto still remains in the role of basic infrastructure in the AI world, failing to truly connect to user experience.

This combination seems to lay the groundwork for the future. What truly ignited this field was the subsequent rise of AI Agent narratives—bringing agents onto the chain, enabling protocols themselves to possess autonomous learning and interaction capabilities, driving a fundamental revolution in application forms.

The Rise of AI Agents: Crypto and AI Moving Towards the Application Layer

As the decentralized computing power market gradually stabilizes, the exploration of Crypto and AI has also shifted from underlying resources to the application layer of intelligent agents. This round of transformation is marked by the rise of on-chain AI Agents, reigniting the market's expectations for the combination of the two.

Initially, AI tokens remained in the cultural phenomenon stage. Many early projects quickly garnered attention with anthropomorphic and entertaining images, completing their cold start through community resonance and emotional diffusion. As user interaction demands increased, AI tokens began to possess preliminary interactive capabilities. On social platforms, AI started to perform simple tasks like content generation and information retrieval in a lightweight agent role, moving from passive display to active response.

Soon, AI Agents will penetrate more vertical application scenarios. Fields such as on-chain finance, NFTs, data analysis, and social companionship have given rise to a large number of specialized intelligent agents. Users are no longer just spectators but can directly participate in on-chain operations, execute strategies, and manage assets through intelligent agents.

The real turning point is the emergence of the Agent framework and execution protocol. Project parties have realized that single-point agents struggle to cope with increasingly complex on-chain demands, leading to the successive birth of multiple modular frameworks. These frameworks support personality modeling, task orchestration, and multi-agent collaboration, allowing on-chain agents to transition from isolated individuals to systematic operations. As a result, Crypto and AI have officially moved from simple application interfaces to the systematic stage of "operating protocols."

Meanwhile, the Agent economy began to take root on the chain. Some projects established standards for autonomous token issuance by agents, protocol collaboration, and social dissemination through the AI Launchpad, promoting the emergence of the "AI native economic entity."

The rise of these projects signifies that Crypto and AI are moving from simple resource matching to the construction of on-chain economic systems, and from single-point functions to the reshaping of native finance and social structures.

Moving Towards Collaboration and Standardization: New Protocols and Their New Directions

As the early wave subsides, Crypto and AI are undergoing a profound reshuffle. The market's total market capitalization has significantly decreased from its peak, and the creation of new agents has also sharply declined. This shift signifies a change in market sentiment—from chasing narratives back to seeking true product-market fit.

In this context, an open standard protocol born for AI applications has become the new catalyst that best meets current demands. This protocol aims to unify the communication methods between large language models and external data and tools, allowing any language model to uniformly and securely access external data sources and tools without the need for complex, repetitive custom integration development.

The application ecosystem around this protocol is also rapidly emerging. Some projects rely on trusted execution environments to provide secure and scalable Computing Power support for the protocol applications, while others build a unified data layer for AI applications in Web3 by extending the protocol, aggregating multi-chain data access and Agent deployment.

More importantly, this protocol opens up new directions for the future of Crypto and AI:

1. Multi-Agent Collaboration: Through this protocol, agents can collaborate according to their functional division, combining to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, thereby enhancing overall efficiency and reliability.

2. On-chain transaction automation: This protocol links various transactions and risk control Agents, addressing many issues in traditional Web3 and achieving safer and more efficient on-chain asset management.

3. Rise of Information Finance (InfoFi): Based on this protocol, intelligent agents not only execute operations but also intelligently plan profit paths according to user profiles, promoting a new financial model that shifts from capital flow to information flow.

Summary: The Long Evolution of the Agent Economy

Looking back, the integration of Crypto and AI has been a long journey of deepening functionality and enhancing practicality. From the initial entertainment chatbots to the gradually emerging Alpha analysis and tool-based agents, and then to the intelligent agents that directly encapsulate natural language into on-chain financial operations, each leap has brought AI Agents closer to the demands of the real world.

Overall, this is a clear and progressive context: entertainment dialogue agent → tool dialogue agent → trading execution agent → decentralized finance artificial intelligence abstraction layer → collective intelligence and multi-agent collaboration. Because of this, the future of AI Agents is no longer a simple narrative-driven approach, but must be built on a foundation of real practicality. This path will be longer than any previous narrative cycle, but also because of the continuously accumulated practical support, the limits it can unlock far exceed imagination.